Top Senior Data Engineer Jobs in Atlanta, GA

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

As a Manager in Managed Services, you'll lead data ecosystems projects, mentor juniors, manage client accounts, and deliver data strategies to optimize operations and drive insights for clients.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Senior Data Engineer on PwC's Managed Data, Analytics & Insights team to design, build and manage advanced data ecosystems. Responsibilities include designing data solutions and scalable pipelines, solving complex problems, mentoring junior staff, maintaining high delivery standards, and building client relationships while aligning solutions to business context.

Lead data engineering efforts within Technology Consulting: design data architecture and pipelines, implement AWS/Redshift and ETL solutions, support BI (QlikView/Oracle BI), coach teams, manage client relationships and SLAs, apply systems thinking to optimize outcomes and validate solutions with stakeholders.

Lead data engineering engagements to design, build, and maintain ETL/ELT pipelines and cloud data architectures. Manage client accounts and mentor teams, leverage tools like DataStage, AWS/Redshift, DB2/SQL Server, GoldenGate, and BI/visualization platforms to deliver analytics, performance tuning, and scalable reporting solutions.

The Data Engineer role involves developing and maintaining data pipelines for forecasting in commercial real estate, ensuring data integrity and supporting analytics teams.

Build, deploy, and operate Azure-based data integration and analytics solutions using Databricks, Delta Lake, ADF, and Synapse. Implement ingestion, transformation, data models, governance, performance optimization, CI/CD, and MLOps patterns while collaborating with architects, offshore teams, and business stakeholders.

The Principal Data Engineer leads the development of scalable data solutions, collaborates on product roadmaps, influences organizational change, and coaches junior staff in a hybrid work environment.

Lead the design and migration of a data platform architecture. Manage data pipelines, mentor teams, and ensure platform reliability.

Build and maintain centralized pricing data infrastructure, scalable workflows, and data pipelines; integrate AI capabilities; deliver pricing tools, reporting, and governance to support data-driven pricing and budgeting across the firm.

Design, build, and maintain scalable data systems and pipelines (batch and streaming); perform complex data modeling and transformations; implement data architectures, governance, and automated deployment pipelines; collaborate with analytics and stakeholder teams to deliver robust, production-ready data products supporting trading and agricultural analytics.

Designs and implements scalable cloud-based data pipelines and architectures, defines technical scope with stakeholders, monitors production reliability, mentors junior engineers, collaborates with data scientists and analysts, and creates reusable best practices and assets to deliver trusted data for decision-making.

The Senior Data Engineer designs and maintains data systems, develops data pipelines for ingestion and analysis, and optimizes data architecture to support business needs.

Build, operate, and monitor ETL/ELT pipelines between marketing, sales, and operational systems into Databricks. Implement dimensional models, identity resolution, data quality checks, API integrations, and scheduled/event-driven syncs to support segmentation, attribution, and campaign activation. Maintain documentation, alerting, and runbooks.

Design, build, test, and operationalize complex data pipelines and transformations using ETL tools and cloud platforms. Ensure data quality, governance, and security while collaborating across teams, troubleshooting incidents, and mentoring engineers to deliver scalable data solutions for analytics and ML.

Design, build, operate, and support ETL/ELT pipelines, integrations, and curated datasets across enterprise systems (Salesforce, Deltek, Autodesk, Azure, Microsoft Fabric). Implement data models, orchestration, API-first services, data quality, governance, and monitoring to support BI, reporting, AI/ML, and secure data delivery.

Design and build scalable data lakes, warehouses, and lakehouses. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest data from third-party APIs, optimize columnar file formats and SQL, support ML initiatives, and consult with stakeholders to translate business needs into data architecture roadmaps.

Lead delivery of large-scale data projects (pipelines, warehouses, lakes, analytics). Guide data modeling, ETL/ELT, and cloud data architectures. Coordinate onshore/offshore teams, manage stakeholders, enforce data governance, quality, and security, and track delivery metrics and risks to ensure projects meet scope, schedule, and budget.

As a Lead Data Engineer, you will architect data foundations for AI applications, lead a team, optimize data pipelines, and ensure data governance.

Design, build, and operate large-scale Hadoop-based data platforms and pipelines using Spark, Kafka, Hive, and workflow tools. Ensure data quality, performance tuning, monitoring, governance, and mentor junior engineers while automating orchestration and evaluating modern cloud data technologies.

Design, build, and maintain enterprise data pipelines, stores, and microservices using big-data tools and cloud platforms. Collaborate with data scientists, modelers, and stakeholders to prepare, transform, and serve scalable, compliant data assets for analytics, reporting, and business decision-making.

Design, build, and maintain data warehouses, data marts, and OLAP solutions; develop ETL frameworks, data models, SSIS/SSAS/SSRS artifacts, and performance-tuned SQL Server databases to support analytics and visualization projects.